Predicting Students’ Course Performance Based on Learners’ Characteristics via Fuzzy Modelling Approach

Teh, Chee Siong and Lee, Shu Hsien and Mohamad Hardyman, Barawi (2019) Predicting Students’ Course Performance Based on Learners’ Characteristics via Fuzzy Modelling Approach. International Journal on Advanced Science, Engineering and Information Technology, 9 (6). pp. 1944-1949. ISSN 2088-5334

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Frequent assessment allows instructors to ensure students have met the course learning objectives. Due to lack of instructor-student interaction, most of the assessment feedbacks and early interventions are not carried out in the large class size. This study is to proposes a new way of assessing student course performance using a fuzzy modeling approach. The typical steps in designing a fuzzy expert system include specifying the problem, determining linguistic variables, defining fuzzy sets as well as obtaining and constructing fuzzy rules is deployed. An educational expert is interviewed to define the relationship between the factors and student course performance. These steps help to determine the range of fuzzy sets and fuzzy rules in fuzzy reasoning. After the fuzzy assessing system has been built, it is used to compute the course performances of the students. The subject expert is asked to validate and verify system performance. Findings show that the developed system provides a faster and more effective way for instructors to assess the course performances of students in large class sizes. However, in this study, the system is developed based on 150 historical student data and only a total of six factors related to course performance are considered. It is expected that considering more historical student data and adding more factors as the variables help to increase the accuracy of the system.

Item Type: Article
Additional Information: [1] R. M. Gagne, W. W. Wager, K. C. Golas, J. M. Keller, and J. D. Russell, “Principles of instructional design,” Performance improvement, vol 44(2), pp. 44-46, 2005. [2] C. M. Cranston, and B. McCort, “A learner analysis experiment: Cognitive style versus learning style in undergraduate nursing education,” Journal of nursing education, vol. 24(4), pp. 136-138, 1985. [3] C. Larmuseau, J. Elen, and F. Depaepe, “The influence of students' cognitive and motivational characteristics on studentsúse of a 4C/ID-based online learning environment and their learning gain,” in Proceedings of the 8th International Conference on Learning Analytics and Knowledge, 2018, p. 171-180, ACM. [4] M. J. Kintu, C. Zhu, and E. Kagambe, “Blended learning effectiveness: the relationship between student characteristics, design features and outcomes,” International journal of educational technology in higher education, vol. 14(1), pp. 7, 2017. [5] R. F. Kizilcec, M. Pérez-Sanagustín, and J. J. Maldonado, “Selfregulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses,” Computers & education, vol. 104, pp. 18-33, 2017. [6] M. L. Tan and J. Harun, “Utilizing Concept Map of the Social Presence Characteristics in Social Collaborative Learning Environment for Nurturing Engineering Students' Knowledge Construction Levels,” Advanced science letters, vol. 24(6), pp. 45614564, 2018. [7] R. Arredondo, “Multiple Regression Analysis of Student's Learning Style, Student's Characteristics and Learning Environment to Predict Student's Performance in an Introductory Accounting Course,” Doctoral dissertation, Northcentral University, 2018. [8] M. D. Price and S. E. Park, “Can Noncognitive Components of Admissions Data Predict Dental Student Performance and Postdoctoral Program Placement?” Journal of dental education, vol. 82(10), pp. 1051-1058, 2018. [9] M. Celepkolu and K. E. Boyer, “Predicting Student Performance Based on Eye Gaze During Collaborative Problem Solving,” Proceedings of Group Interaction Frontiers in Technology (GIFT performance using data mining techniques,” International journal of pure and applied mathematics, vol. 119(12), pp. 221-227, 2018. [11] R. L. Ulloa-Cazarez, C. López-Martín, A. Abran, and C. YáñezMárquez, “Prediction of online students performance by means of genetic programming,” Applied artificial intelligence, vol. 32(9-10), pp. 858-881, 2018. [12] E. Jembere, R. Rawatlal, and A. W. Pillay, “Matrix Factorisation for Predicting Student Performance,” in 7th World Engineering Education Forum (WEEF), IEEE, pp. 513-518, (2017, November). [13] Z. Jeremić, J. Jovanović, and D. Gašević, “Student modeling and assessment in intelligent tutoring of software patterns,” Expert systems with applications
Uncontrolled Keywords: assessment feedback; fuzzy logic; fuzzy inference system; student modeling; intelligent tutoring system, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, , research, Universiti Malaysia Sarawak.
Subjects: L Education > L Education (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Faculties, Institutes, Centres > Faculty of Cognitive Sciences and Human Development
Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Barawi
Date Deposited: 07 Jan 2020 08:40
Last Modified: 24 Aug 2020 06:46

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